CSA, frequently presenting as Cheyne-Stokes respiration (CSR), is highly prevalent in CHF and is primarily driven by instability of ventilatory control, characterized by increased loop gain^[9,10]. During sleep, respiratory drive depends largely on chemoreceptor responsiveness to PaCO₂. In CHF, pulmonary congestion activates pulmonary J receptors, leading to chronic hyperventilation and a reduction in PaCO₂ toward or below the apneic threshold^[11,15].

Consequently, even minor fluctuations in ventilation - such as post-arousal hyperventilation - can lower PaCO₂ below the threshold required to sustain respiratory drive, triggering central apnea. During apnea, PaCO₂ gradually rises until it exceeds the threshold, inducing compensatory hyperventilation. This feedback loop generates the characteristic periodic breathing pattern of CSA/CSR^[9,36].

Reduced cardiac output further contributes by prolonging circulatory delay between the lungs and central chemoreceptors. This delay introduces a temporal mismatch between blood gas changes and ventilatory responses, thereby amplifying instability in respiratory control and determining the cycle length of CSR^[37,38].

Nocturnal rostral fluid shift represents another key mechanism. Redistribution of fluid from the lower extremities to the thorax and neck in the supine position is accentuated in CHF^[12]. Fluid accumulation in the neck increases peripharyngeal tissue pressure and upper airway collapsibility, promoting OSA^[39,40]. Simultaneously, pulmonary fluid accumulation worsens congestion, stimulates vagal C-fibers, and induces hyperventilation with further reductions in PaCO₂, thereby facilitating CSA^[11,13]. This fluid shift provides a unifying pathophysiological link between OSA and CSA and may explain the dynamic transition between obstructive and central events observed in some patients during sleep^[12,14].

SDB contributes to CHF progression through interrelated mechanisms including neurohumoral activation, hemodynamic stress, intermittent hypoxia, oxidative stress, and systemic inflammation. These processes act synergistically to promote adverse cardiac remodeling and end-organ dysfunction.

Several validated tools may be used for initial risk stratification in ambulatory patients with CHF. The Epworth Sleepiness Scale (ESS) assesses subjective daytime sleepiness, with scores ≥ 10 suggesting excessive sleepiness and the need for further evaluation^[65]. However, its sensitivity may be limited in CHF because fatigue often predominates over overt sleepiness.

The STOP-BANG (Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index, Age, Neck circumference, and Gender) questionnaire evaluates snoring, tiredness, observed apnea, high blood pressure, body mass index, age, neck circumference, and male sex. A score ≥ 3 indicates increased risk of OSA, although specificity may be reduced in CHF because several risk factors are common in this population^[66].

Sleep quality and insomnia can be assessed using the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI). A PSQI score > 5 indicates poor sleep quality, whereas an ISI score ≥ 15 suggests at least moderate insomnia^[67,68]. Sleep diaries recorded over 1-2 weeks may provide additional information on sleep patterns, nocturnal awakenings, and sleep efficiency, particularly when insomnia or circadian rhythm disturbances are suspected^[69,70].

In clinical practice, “high risk” should be determined using a combination of questionnaire results and clinical features rather than a single screening tool. Patients with CHF may be considered high risk if they have STOP-BANG ≥ 3, ESS score ≥ 10, PSQI > 5, ISI ≥ 15, or clinical features such as witnessed apnea, habitual snoring, nocturnal choking, refractory fatigue, recurrent nocturnal dyspnea, atrial fibrillation, resistant hypertension, pulmonary hypertension, obesity, or suspected CSA^[1,64-68]. Because symptoms may be atypical or absent in CHF, clinicians should maintain a low threshold for objective sleep testing^[64].

Given the limitations of symptom-based screening, objective testing is essential. Portable respiratory polygraphy represents a pragmatic and cost-effective option for both outpatient and inpatient assessment^[64,71]. Standard measurements include airflow, respiratory effort, oxygen saturation, heart rate or rhythm, body position, and snoring.

Clinical studies highlight the high burden of SDB in CHF. In a cohort of patients with non-ischemic CHF, Damy et al. reported SDB in 85% of patients (AHI ≥ 5 events/h), with predominantly obstructive events. AHI was independently associated with left ventricular hypertrophy, underscoring the structural impact of untreated SDB^[71]. Similarly, increasing SDB severity has been associated with biomarkers of myocardial injury and metabolic dysregulation, supporting the role of objective testing in risk stratification^[72].

Home respiratory polygraphy demonstrates reasonable diagnostic agreement with in-laboratory Polysomnography (PSG) and offers advantages in accessibility, cost, and ecological validity^[73]. However, the absence of electroencephalographic data limits accurate assessment of sleep stages and total sleep time, potentially leading to underestimation of AHI, particularly in patients with fragmented sleep^[73].

PSG remains the gold standard for diagnosing SDB. It provides a comprehensive evaluation of sleep architecture, respiratory events, oxygen desaturation, cardiac rhythm, body position, and limb movements^[74]. Compared with respiratory polygraphy, PSG offers several advantages in CHF. Electroencephalographic monitoring enables precise sleep staging and detection of arousal-related events. It also allows reliable differentiation between OSA and CSA, which is critical given their distinct therapeutic implications. In addition, PSG can identify coexisting sleep disorders, such as periodic limb movement disorder or Rapid Eye Movement‌ (REM) sleep behavior disorder, which may contribute to sleep fragmentation^[74,75]. Despite these advantages, PSG is resource-intensive, requires specialized infrastructure, and is often associated with long waiting times, which may delay diagnosis and prolong exposure to untreated SDB.

Available evidence supports a stepwise screening approach in CHF. Questionnaires such as ESS, STOP-BANG, and PSQI may assist with initial risk stratification, but their limitations necessitate a low threshold for objective testing^[3,64-67]. Portable respiratory polygraphy is a practical first-line modality in many patients, while PSG should be reserved for diagnostically complex cases, suspected CSA, inconclusive results, or suspected coexisting sleep disorders. A pragmatic pathway combining feasibility and diagnostic accuracy is essential for implementation in routine care. For ease of clinical application, the principal screening questionnaires, diagnostic tools, and emerging technologies discussed in this review are summarized in Table 1.

Several emerging technologies may facilitate accessible and longitudinal screening for sleep disorders in CHF, although each has specific strengths and limitations. Home Sleep Apnea Testing (HSAT) and portable respiratory polygraphy provide lower-cost, home-based assessment and have acceptable diagnostic performance for moderate-to-severe OSA^[76]. However, the absence of electroencephalography limits sleep staging, may underestimate AHI, and reduces accuracy in distinguishing OSA from CSA^[73,75].

Wearable devices and patch-based systems use physiological signals such as photoplethysmography, accelerometry, heart rate variability, electrocardiography, and respiratory monitoring to estimate sleep and respiratory parameters^[77-79]. Their advantages include convenience, patient acceptability, and the ability to support repeated longitudinal assessment. However, performance varies among devices, validation in CHF remains limited, and diagnostic accuracy is generally lower than PSG^[75,77,79].

Smartphone-based applications and non-contact radar systems offer inexpensive and unobtrusive monitoring without specialized equipment. However, these technologies remain primarily screening tools because their performance may be affected by environmental factors, motion artifacts, and limited validation data^[80-82].

Overall, emerging technologies may support preliminary screening and follow-up, but PSG remains the reference standard when CSA, mixed sleep apnea, inconclusive HSAT findings, or coexisting sleep disorders are suspected^[75,76].

Given the high prevalence and clinical impact of SDB in CHF, a structured and pragmatic screening pathway is essential.

Insomnia is characterized by persistent dissatisfaction with sleep quantity or quality, including difficulty initiating or maintaining sleep or early morning awakening, accompanied by daytime impairment such as fatigue, impaired concentration, and mood disturbances^[68]. In CHF, insomnia is often associated with anxiety, depression, and disease-related psychological stress, and patients are typically able to describe their sleep difficulties clearly^[3].

In contrast, SDB is characterized by abnormal respiratory events during sleep. OSA typically presents with loud snoring, witnessed apneas, nocturnal choking, and excessive daytime sleepiness^[13,15]. However, these classic symptoms are less predictive in CHF populations and may contribute to under-recognition^[64]. CSA, particularly in the form of Cheyne-Stokes respiration, is more common in advanced CHF and often presents with periodic breathing, nocturnal dyspnea, and recurrent awakenings rather than prominent snoring or hypersomnolence^[52]. A key distinguishing feature is that insomnia reflects intrinsic hyperarousal, whereas sleep fragmentation in SDB is secondary to recurrent respiratory events and arousals. Information from bed partners regarding snoring, apneas, or abnormal breathing patterns may therefore be particularly valuable.

Standardized questionnaires may support initial evaluation but have limited diagnostic accuracy in CHF. The ESS evaluates daytime sleepiness, while the PSQI and ISI assess sleep quality and insomnia severity. STOP-BANG and Berlin questionnaires may help identify patients at increased risk of OSA, although specificity is reduced in CHF populations^[65-68].

PSG remains the gold standard for diagnosing SDB and distinguishing OSA from CSA. It provides comprehensive assessment of sleep architecture, respiratory events, oxygen saturation, and cardiac rhythm, enabling precise characterization of apnea type and severity^[75]. In CSA, PSG typically demonstrates absent respiratory effort during apneic episodes and Cheyne-Stokes respiration patterns^[13,15]. PSG also facilitates identification of alternative causes of sleep fragmentation, such as periodic limb movement disorder or REM sleep behavior disorder^[75].

Portable respiratory polygraphy and HSAT are increasingly used because of their accessibility and feasibility in outpatient settings. In patients with a high pre-test probability of moderate-to-severe OSA, positive HSAT findings may be sufficient to establish the diagnosis and initiate treatment^[76]. However, the absence of electroencephalographic monitoring limits sleep stage assessment, may underestimate AHI, and reduces accuracy in differentiating OSA from CSA. Emerging technologies, including wearable devices, smartphone-based applications, and radar-based systems, may support preliminary screening and longitudinal monitoring but currently lack sufficient validation for definitive diagnosis^[77,80,81].

A practical diagnostic pathway is summarized in Figure 1. Initial evaluation should include targeted clinical history, ideally incorporating bed partner observations, together with standardized questionnaires. Patients with suspected SDB should undergo objective testing, with HSAT serving as an appropriate first-line modality in selected patients and PSG reserved for inconclusive results, suspected CSA, mixed sleep apnea, or coexisting sleep disorders^[65-67].

Mixed sleep apnea is common in CHF, with patients exhibiting both obstructive and central respiratory events or transitioning between patterns over time. This likely reflects dynamic changes in fluid distribution, sleep stage, and chemoreceptor sensitivity and often requires full-night PSG for accurate characterization^[12].

Coexistence of insomnia and SDB is also frequent. Therefore, diagnosis should integrate subjective symptoms with objective findings. Patients with insomnia complaints but elevated AHI should be considered to have SDB with comorbid insomnia and may require combined management strategies^[3].

Assessment of cognition and mood may further aid differentiation. SDB is more strongly associated with impaired attention and executive function, whereas insomnia is often linked to subjective memory complaints and emotional dysregulation^[3].

A structured, stepwise diagnostic approach is recommended to facilitate clinical implementation and is illustrated in Figure 1. Initial evaluation should include targeted clinical history - ideally incorporating bed partner observations - together with standardized questionnaires such as the ESS, PSQI, and STOP-BANG. An ESS score ≥ 10 or STOP-BANG score ≥ 3 increases the likelihood of SDB, while a PSQI score > 5 indicates clinically significant sleep disturbance warranting further assessment.

Patients with suspected SDB should undergo objective testing. In those with a high pre-test probability of moderate-to-severe OSA, HSAT represents an appropriate first-line modality. However, referral for PSG is recommended when HSAT results are negative or inconclusive despite persistent clinical suspicion, when CSA is suspected - particularly in patients with reduced ejection fraction or atrial fibrillation - or when coexisting sleep disorders are likely.

PSG should be used to confirm the diagnosis and define the SDB phenotype. Key parameters include AHI, the type and distribution of respiratory events, their relationship to sleep stages, patterns of oxygen desaturation, and the presence of Cheyne-Stokes respiration. In addition, PSG enables exclusion of alternative causes of sleep disruption, which is essential for guiding appropriate management.

CPAP remains the cornerstone therapy for OSA. By preventing upper airway collapse, CPAP reduces respiratory events, intermittent hypoxemia, arousal-related sympathetic activation, and negative intrathoracic pressure swings^{[13,15,87,88]}. These effects may improve oxygenation, reduce cardiac afterload, and attenuate neurohumoral activation. Meta-analyses have demonstrated improvements in AHI, oxygenation, and Left Ventricular Ejection Fraction (LVEF) in selected patients with CHF and OSA^[87]. Clinical studies have also reported improvements in symptoms, functional status, and daytime sleepiness^[88]. Adherence remains a major determinant of treatment effectiveness, highlighting the importance of patient education, follow-up, and individualized device adjustment. Alternative modalities such as APAP and Bilevel Positive Airway Pressure (BiPAP) may be considered in selected patients with CPAP intolerance^[87].

CSA in CHF is often a manifestation of underlying cardiac dysfunction and is driven by ventilatory instability, hypocapnia, pulmonary congestion, and prolonged circulatory delay^[52]. Accordingly, management should target these underlying mechanisms. Because CSA frequently reflects the severity of heart failure, optimization of heart failure therapy, decongestion, and improvement of cardiac output remain the central therapeutic priorities^[1,52,57].

Although ASV effectively suppresses central respiratory events, the SERVE-HF (Treatment of Sleep-Disordered Breathing with Predominant Central Sleep Apnoea by Adaptive Servo Ventilation in Patients with Heart Failure) trial demonstrated increased all-cause and cardiovascular mortality in patients with Heart Failure with reduced Ejection Fraction (HFrEF) (LVEF ≤ 45%), leading to a contraindication in this population^[89,90]. In patients with Heart Failure with preserved Ejection Fraction (HFpEF), ASV may improve respiratory parameters such as AHI and oxygenation, although evidence for improvement in clinical outcomes remains limited^[91]. Supplemental oxygen and selected respiratory stimulants may improve ventilatory stability in selected patients, but their role remains uncertain because outcome data are limited^[52]. Close collaboration between cardiology and sleep medicine specialists is therefore essential.

Guideline-directed medical therapy (GDMT) remains the foundation of CHF management and may indirectly improve SDB through optimization of cardiac function and hemodynamics^[57]. Improved cardiac output, reduced pulmonary congestion, attenuation of neurohormonal activation, and reduced rostral fluid redistribution may lessen both CSA-promoting ventilatory instability and OSA-promoting upper airway edema^{[1,12,13,39,57]}. Therapies targeting the renin-angiotensin system, including Angiotensin Converting Enzyme (ACE) inhibitors, Angiotensin Receptor Blockers (ARBs), and angiotensin receptor-neprilysin inhibitors, may improve cardiac loading conditions and reduce pulmonary congestion^[1]. In addition, sodium-glucose cotransporter-2 inhibitors (SGLT2i) have been associated with improvements in cardiac performance, metabolic status, and body weight, which may contribute to reductions in OSA severity^[58]. Because polypharmacy is common in CHF, careful medication review, individualized titration, and regular reassessment are important to maximize both cardiovascular and sleep-related benefits.

In summary, management of sleep disorders in CHF requires a phenotype-driven and multidisciplinary approach. CBT-I is recommended as first-line therapy for insomnia, CPAP remains the cornerstone treatment for OSA, and CSA management should focus on optimization of heart failure therapy with careful patient selection for advanced interventions. Integration of sleep and heart failure management may improve both quality of life and long-term outcomes. A summary of phenotype-specific treatment strategies, mechanistic targets, and key clinical considerations is provided in Table 2.

Cardiologists should screen for sleep disorders, recognize HF-specific patterns (e.g., CSA), optimize GDMT, and guide treatment decisions. Sleep specialists confirm diagnosis and manage Positive Airway Pressure (PAP) therapy, while respiratory therapists support device initiation and adherence. Nursing staff contribute to screening, non-pharmacological interventions, and patient education. Clinical pharmacists assist with medication optimization and safety^{[1,34,62,94-96]}.

Patient and caregiver engagement is essential for successful implementation of sleep disorder management in CHF. Sleep disturbances frequently affect both patients and their caregivers, and a dyadic care approach may improve adherence and clinical outcomes^[35].

Effective education should encompass disease awareness, engagement in screening processes, realistic treatment expectations, and the development of self-monitoring skills, while actively involving caregivers in the management process. Improved understanding has been associated with greater diagnostic uptake and enhanced long-term adherence to therapy^[2].

The clinical effectiveness of PAP therapy is highly dependent on sustained adherence. Suboptimal adherence is associated with limited improvement in cardiac function and clinical outcomes^[97-99]. Contemporary PAP devices allow remote monitoring of usage patterns, mask leak, and residual AHI, enabling early identification of adherence barriers and timely intervention^[94].

Adherence is commonly defined as use for at least 4 hours per night on 70% or more of nights^[99]. Strategies to improve adherence should include early and structured follow-up, proactive management of side effects, telemonitoring, patient education, individualized device optimization, and psychological support. Implementation of such comprehensive approaches has been shown to improve both adherence and clinical outcomes^[94,99].

Cardiac rehabilitation provides an effective platform for integrating sleep disorder management into routine CHF care^[63]. Regular physical activity has been associated with improved sleep quality in CHF, with higher activity levels correlating with better sleep outcomes^[99]. Moreover, improved daytime activity may reinforce circadian regulation, creating a beneficial feedback loop between sleep quality and functional capacity^[63].

Psychological support components of rehabilitation programs offer an opportunity to incorporate CBT-I, which has been shown to improve insomnia symptoms, fatigue, mood, and functional status in CHF populations^[62]. In addition, dietary patterns such as the Mediterranean diet have been associated with improved sleep quality and reduced insomnia symptoms, suggesting a broader role for lifestyle interventions in sleep management^[100].

Structured, longitudinal follow-up is critical to ensure sustained treatment effects. Drawing on established frameworks for chronic disease management and PAP adherence monitoring^[73], a phased approach may be conceptualized, including an early baseline phase (0-1 month) focused on treatment initiation and education, an adaptation phase (1-3 months) addressing adherence and early response, a consolidation phase (3-12 months) reinforcing behavioral and therapeutic stability, and a long-term maintenance phase beyond 12 months. Such structured follow-up models have been associated with improved adherence and better clinical outcomes in patients with SDB^[94,99].

To ensure effective implementation, standardized quality indicators should be incorporated into clinical practice. Consistent with established quality improvement frameworks in cardiovascular care¹, these include process measures (e.g., screening rates, diagnostic completion), intermediate outcomes (e.g., adherence, symptom improvement), and clinical outcomes (e.g., hospitalization, functional status, mortality). Integration of these metrics into existing heart failure quality frameworks may enhance care delivery and support continuous quality improvement^[1,93].